{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fifty-saskatchewan",
   "metadata": {
    "papermill": {
     "duration": 0.04612,
     "end_time": "2021-03-18T08:36:54.438269",
     "exception": false,
     "start_time": "2021-03-18T08:36:54.392149",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR10\" # CIFAR10, CIFAR100, SVHN\n",
    "v_type = \"mingd\" # mingd, rand\n",
    "root_path = \"\"\n",
    "params_path = \"\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "unusual-comparative",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/mohammad/transferability/src/camera_ready\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "continued-pacific",
   "metadata": {
    "papermill": {
     "duration": 0.035971,
     "end_time": "2021-03-18T08:36:54.508235",
     "exception": false,
     "start_time": "2021-03-18T08:36:54.472264",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"SVHN\"\n",
    "v_type = \"rand\"\n",
    "root_path = \"/home/mohammad/transferability/files\"\n",
    "params_path = \"/home/mohammad/transferability/src\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "subject-bacteria",
   "metadata": {
    "papermill": {
     "duration": 0.050008,
     "end_time": "2021-03-18T08:36:54.591331",
     "exception": false,
     "start_time": "2021-03-18T08:36:54.541323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# SVHN - Blind Walk results"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown\n",
    "v_type2disp = dict(mingd=\"MinGD\", rand=\"Blind Walk\")\n",
    "display(Markdown(f\"# {dataset} - {v_type2disp[v_type]} results\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "coral-satin",
   "metadata": {
    "papermill": {
     "duration": 0.501097,
     "end_time": "2021-03-18T08:36:55.127576",
     "exception": false,
     "start_time": "2021-03-18T08:36:54.626479",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.insert(0, params_path)\n",
    "\n",
    "import argparse, params, time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from importlib import reload\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import random\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "introductory-investigator",
   "metadata": {
    "papermill": {
     "duration": 0.615328,
     "end_time": "2021-03-18T08:36:55.778321",
     "exception": false,
     "start_time": "2021-03-18T08:36:55.162993",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "integrated-latter",
   "metadata": {
    "papermill": {
     "duration": 0.037798,
     "end_time": "2021-03-18T08:36:55.853327",
     "exception": false,
     "start_time": "2021-03-18T08:36:55.815529",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import combine_pvalues, ttest_ind_from_stats, ttest_ind\n",
    "from functools import reduce\n",
    "from scipy.stats import hmean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "silver-barcelona",
   "metadata": {
    "papermill": {
     "duration": 0.029453,
     "end_time": "2021-03-18T08:36:55.917581",
     "exception": false,
     "start_time": "2021-03-18T08:36:55.888128",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Use the full vectors to train a linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "weighted-authorization",
   "metadata": {
    "papermill": {
     "duration": 0.036031,
     "end_time": "2021-03-18T08:36:55.982756",
     "exception": false,
     "start_time": "2021-03-18T08:36:55.946725",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root = os.path.join(root_path,dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "colored-exclusive",
   "metadata": {
    "papermill": {
     "duration": 0.050393,
     "end_time": "2021-03-18T08:36:56.067404",
     "exception": false,
     "start_time": "2021-03-18T08:36:56.017011",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\" and dataset == \"CIFAR100\": v_type = \"topgd\"\n",
    "    \n",
    "names = ['teacher','random',\"distillation\",\"extract-label\",\"extract-logit\",\"zero-shot\",\"pre-act-18\",\"fine-tune\",\"independent\"]\n",
    "trains = {}\n",
    "tests = {}\n",
    "for name in names:\n",
    "    trains[name] = (torch.load(f\"{root}/model_{name}_normalized/train_{v_type}_vulnerability.pt\"))\n",
    "    tests[name] = (torch.load(f\"{root}/model_{name}_normalized/test_{v_type}_vulnerability.pt\"))\n",
    "mean_cifar = trains[\"teacher\"].mean(dim = (0,1))\n",
    "std_cifar = trains[\"teacher\"].std(dim = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "impossible-stability",
   "metadata": {
    "papermill": {
     "duration": 0.052768,
     "end_time": "2021-03-18T08:36:56.157074",
     "exception": false,
     "start_time": "2021-03-18T08:36:56.104306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\":\n",
    "    for name in names:\n",
    "        trains[name] = trains[name].sort(dim = 1)[0]\n",
    "        tests[name] = tests[name].sort(dim = 1)[0]\n",
    "        \n",
    "for name in names:\n",
    "    trains[name] = (trains[name]- mean_cifar)/std_cifar\n",
    "    tests[name] = (tests[name]- mean_cifar)/std_cifar\n",
    "    \n",
    "f_num = 30\n",
    "a_num=30\n",
    "\n",
    "trains_n = {}\n",
    "tests_n = {}\n",
    "for name in names:\n",
    "    trains_n[name] = trains[name].T.reshape(1000,f_num)[:,:a_num]\n",
    "    tests_n[name] = tests[name].T.reshape(1000,f_num)[:,:a_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "painful-click",
   "metadata": {
    "papermill": {
     "duration": 0.038815,
     "end_time": "2021-03-18T08:36:56.232165",
     "exception": false,
     "start_time": "2021-03-18T08:36:56.193350",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_ex = split_index\n",
    "train = torch.cat((trains_n[\"teacher\"][:n_ex], tests_n[\"teacher\"][:n_ex]), dim = 0)\n",
    "y = torch.cat((torch.zeros(n_ex), torch.ones(n_ex)), dim = 0)\n",
    "\n",
    "rand=torch.randperm(y.shape[0])\n",
    "train = train[rand]\n",
    "y = y[rand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "opposite-binary",
   "metadata": {
    "papermill": {
     "duration": 0.039479,
     "end_time": "2021-03-18T08:36:56.308735",
     "exception": false,
     "start_time": "2021-03-18T08:36:56.269256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = nn.Sequential(nn.Linear(a_num,100),nn.ReLU(),nn.Linear(100,1),nn.Tanh())\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer =torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "adverse-training",
   "metadata": {
    "papermill": {
     "duration": 2.438523,
     "end_time": "2021-03-18T08:36:58.782199",
     "exception": false,
     "start_time": "2021-03-18T08:36:56.343676",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "17f92fb928a34d088f6c42fb5f44a3e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tqdm(range(1000)) as pbar:\n",
    "    for epoch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        inputs = train\n",
    "        outputs = model(inputs)\n",
    "        loss = -1 * ((2*y-1)*(outputs.squeeze(-1))).mean()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        pbar.set_description('loss {}'.format(loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impossible-lunch",
   "metadata": {
    "papermill": {
     "duration": 0.041013,
     "end_time": "2021-03-18T08:36:58.860604",
     "exception": false,
     "start_time": "2021-03-18T08:36:58.819591",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "criminal-cemetery",
   "metadata": {
    "papermill": {
     "duration": 0.064144,
     "end_time": "2021-03-18T08:36:58.974289",
     "exception": false,
     "start_time": "2021-03-18T08:36:58.910145",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_p(outputs_train, outputs_test):\n",
    "    pred_test = outputs_test[:,0].detach().cpu().numpy()\n",
    "    pred_train = outputs_train[:,0].detach().cpu().numpy()\n",
    "    tval, pval = ttest_ind(pred_test, pred_train, alternative=\"greater\", equal_var=False)\n",
    "    if pval < 0:\n",
    "        raise Exception(f\"p-value={pval}\")\n",
    "    return pval\n",
    "\n",
    "def get_p_values(num_ex, train, test, k):\n",
    "    total = train.shape[0]\n",
    "    sum_p = 0\n",
    "    p_values = []\n",
    "    positions_list = []\n",
    "    for i in range(k):\n",
    "        positions = torch.randperm(total)[:num_ex]\n",
    "        p_val = get_p(train[positions], test[positions])\n",
    "        positions_list.append(positions)\n",
    "        p_values.append(p_val)\n",
    "    return p_values\n",
    "\n",
    "def get_fischer(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return combine_pvalues(p_values, method=\"mudholkar_george\")[1]\n",
    "\n",
    "def get_max_p_value(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return max(p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "capital-barrel",
   "metadata": {
    "papermill": {
     "duration": 0.060065,
     "end_time": "2021-03-18T08:36:59.080967",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.020902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "outputs_tr = {}\n",
    "outputs_te = {}\n",
    "for name in names:\n",
    "    outputs_tr[name] = model(trains_n[name])\n",
    "    outputs_te[name] = model(tests_n[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "otherwise-creation",
   "metadata": {
    "papermill": {
     "duration": 0.040574,
     "end_time": "2021-03-18T08:36:59.161026",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.120452",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "def print_inference(outputs_train, outputs_test):\n",
    "    m1, m2 = outputs_test[:,0].mean(), outputs_train[:,0].mean()\n",
    "    pval = get_p(outputs_train, outputs_test)\n",
    "    print(f\"p-value = {pval} \\t| Mean difference = {m1-m2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "controlling-richards",
   "metadata": {
    "papermill": {
     "duration": 0.038074,
     "end_time": "2021-03-18T08:36:59.236528",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.198454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for name in names:\n",
    "    outputs_tr[name], outputs_te[name] = outputs_tr[name][split_index:], outputs_te[name][split_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "thick-helen",
   "metadata": {
    "papermill": {
     "duration": 0.050696,
     "end_time": "2021-03-18T08:36:59.322855",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.272159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teacher\n",
      "p-value = 1.1173747837123814e-73 \t| Mean difference = 0.9328910112380981\n",
      "random\n",
      "p-value = 7.396649392768272e-19 \t| Mean difference = 0.4461976885795593\n",
      "distillation\n",
      "p-value = 1.5666681829427542e-26 \t| Mean difference = 0.5648118853569031\n",
      "extract-label\n",
      "p-value = 3.366502166956986e-19 \t| Mean difference = 0.4763500392436981\n",
      "extract-logit\n",
      "p-value = 1.960569324919194e-22 \t| Mean difference = 0.5233166217803955\n",
      "zero-shot\n",
      "p-value = 8.888381649676119e-28 \t| Mean difference = 0.5684595704078674\n",
      "pre-act-18\n",
      "p-value = 2.963751233385718e-25 \t| Mean difference = 0.49177244305610657\n",
      "fine-tune\n",
      "p-value = 2.8973566987682062e-33 \t| Mean difference = 0.6362242698669434\n",
      "independent\n",
      "p-value = 0.9999999999511755 \t| Mean difference = -0.34539517760276794\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print(f\"{name}\")\n",
    "    print_inference(outputs_tr[name], outputs_te[name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "curious-headquarters",
   "metadata": {
    "papermill": {
     "duration": 0.029641,
     "end_time": "2021-03-18T08:36:59.390076",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.360435",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Main Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "editorial-jumping",
   "metadata": {
    "papermill": {
     "duration": 0.183198,
     "end_time": "2021-03-18T08:36:59.602547",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.419349",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(f\"{dataset}_{v_type}.h5\"):\n",
    "    total_reps = 40\n",
    "    max_m = 45\n",
    "    total_inner_rep = 100\n",
    "\n",
    "    m_list = [x for x in range(2, max_m, 1)]\n",
    "    p_values_all_threat_models_dict = {}\n",
    "\n",
    "    n_pbar = tqdm(names, leave=False)\n",
    "    for name in n_pbar:\n",
    "        p_vals_per_rep_no = {}\n",
    "        r_pbar = tqdm(range(total_reps), leave=False)\n",
    "        for rep_no in r_pbar:\n",
    "            p_values_list = []\n",
    "            for m in m_list:\n",
    "                p_list = get_p_values(m, outputs_tr[name], outputs_te[name], total_inner_rep)\n",
    "                try:\n",
    "                    hm = hmean(p_list)\n",
    "                except:\n",
    "                    hm = 1.0\n",
    "                p_values_list.append(hm)\n",
    "                pbar.set_description(f\"{rep_no: 2d} {name} m={m: 3d}: f{hm: 0.5f}\")\n",
    "            r_pbar.set_description(f\"rep_no: {rep_no+1}/{total_reps}\")\n",
    "            p_vals_per_rep_no[rep_no] = p_values_list\n",
    "        p_values_all_threat_models_dict[name] = p_vals_per_rep_no\n",
    "        \n",
    "    df_list = []\n",
    "    for name, rep_dict in p_values_all_threat_models_dict.items():\n",
    "        df = pd.DataFrame(rep_dict).reset_index().assign(m=lambda r: r.index+2).drop([\"index\"], axis=1)\n",
    "        df_list.append(pd.melt(df,id_vars=[\"m\"], var_name=\"rep_no\", value_name=\"p_value\").assign(threat_model=name))\n",
    "    results_df = pd.concat(df_list)\n",
    "    \n",
    "    results_df.to_hdf(f\"{dataset}_{v_type}.h5\", v_type)\n",
    "    results_df\n",
    "else:\n",
    "    results_df = pd.read_hdf(f\"{dataset}_{v_type}.h5\", v_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assigned-maple",
   "metadata": {
    "papermill": {
     "duration": 0.030167,
     "end_time": "2021-03-18T08:36:59.670233",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.640066",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "baking-ecuador",
   "metadata": {
    "papermill": {
     "duration": 0.038849,
     "end_time": "2021-03-18T08:36:59.739351",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.700502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['text.latex.preamble'] = r'\\usepackage{times}'\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graphic-knight",
   "metadata": {
    "papermill": {
     "duration": 0.030045,
     "end_time": "2021-03-18T08:36:59.804839",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.774794",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "coupled-zambia",
   "metadata": {
    "papermill": {
     "duration": 0.038206,
     "end_time": "2021-03-18T08:36:59.873289",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.835083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rename_dict = {\n",
    "    \"threat_model\": \"Threat Model\", \n",
    "    \"teacher\": \"Source\", \n",
    "    \"distillation\": \"Distillation\", \n",
    "    \"extract-label\": \"Label-Query\", \n",
    "    \"extract-logit\": \"Logit-Query\", \n",
    "    \"zero-shot\": \"Zero-Shot Learning\",\n",
    "    \"fine-tune\": \"Fine-Tuning\",\n",
    "    \"pre-act-18\": \"Diff. Architecture\",\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "medical-display",
   "metadata": {
    "papermill": {
     "duration": 0.075838,
     "end_time": "2021-03-18T08:36:59.985586",
     "exception": false,
     "start_time": "2021-03-18T08:36:59.909748",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_df = (results_df.rename(columns=rename_dict).replace(rename_dict)\n",
    "           .query('(m >= 5)')\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "extra-feeling",
   "metadata": {
    "papermill": {
     "duration": 0.040636,
     "end_time": "2021-03-18T08:37:00.062055",
     "exception": false,
     "start_time": "2021-03-18T08:37:00.021419",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sizes\n",
    "SMALL_SIZE = 14\n",
    "MEDIUM_SIZE = SMALL_SIZE + 1\n",
    "BIGGER_SIZE = MEDIUM_SIZE + 1\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsi ze of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "sacred-instruction",
   "metadata": {
    "papermill": {
     "duration": 6.350946,
     "end_time": "2021-03-18T08:37:06.450559",
     "exception": false,
     "start_time": "2021-03-18T08:37:00.099613",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'p-value')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n",
    "g = sns.lineplot(x=\"m\", y=\"p_value\", hue=\"Threat Model\", data=plot_df, ax=ax, ci=99)\n",
    "g.axhline(0.05, ls='--', color=\"black\", alpha=0.4)\n",
    "g.axhline(0.01, ls='--', color=\"black\", alpha=0.4)\n",
    "g.set_xlabel(\"Number of Sample Revealed\")\n",
    "g.set_ylabel(\"p-value\")"
   ]
  },
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   "source": [
    "# Table"
   ]
  },
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   "id": "national-georgia",
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    "order = ['teacher',\n",
    "         'distillation',\n",
    "         'pre-act-18',\n",
    "         'zero-shot',\n",
    "         'fine-tune',\n",
    "         'extract-label',\n",
    "         'extract-logit',\n",
    "         'random',\n",
    "         'independent']"
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    "from utils import generate_table\n",
    "tab = generate_table(selected_m=10, outputs_te=outputs_te, outputs_tr=outputs_tr, names=names, order=order)"
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "               mean_diff  p_value\n",
      "teacher            0.950    0.000\n",
      "distillation       0.537    0.003\n",
      "pre-act-18         0.450    0.011\n",
      "zero-shot          0.512    0.001\n",
      "fine-tune          0.581    0.000\n",
      "extract-label      0.513    0.002\n",
      "extract-logit      0.515    0.011\n",
      "random             0.475    0.015\n",
      "independent       -0.322    0.572\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context(\"display.float_format\", lambda x: f\"{x: 0.3f}\"):\n",
    "    print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "familiar-commitment",
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    {
     "name": "stdout",
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     "text": [
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "{} & mean_diff &    p_value \\\\\n",
      "\\midrule\n",
      "teacher       &     0.950 & $10^{-08}$ \\\\\n",
      "distillation  &     0.537 & $10^{-03}$ \\\\\n",
      "pre-act-18    &     0.450 & $10^{-02}$ \\\\\n",
      "zero-shot     &     0.512 & $10^{-03}$ \\\\\n",
      "fine-tune     &     0.581 & $10^{-04}$ \\\\\n",
      "extract-label &     0.513 & $10^{-03}$ \\\\\n",
      "extract-logit &     0.515 & $10^{-02}$ \\\\\n",
      "random        &     0.475 & $10^{-02}$ \\\\\n",
      "independent   &    -0.322 & $10^{-01}$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context(\"display.float_format\", lambda x: f\"{x: 0.3f}\"):\n",
    "    print(tab.to_latex(formatters=dict(mean_diff=\"{:0.3f}\".format, p_value=lambda x: \"$10^{\"+f\"{x:0.0E}\"[2:]+\"}$\"), escape=False))"
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